Industries · Insurance · Malaysia + Singapore

Public AI governance for insurance.

Every day, ChatGPT, Gemini, and Google AI Overview answer questions about your policies. Some answers are incomplete, outdated, or wrong. Lawnise tells you what they're saying — and helps you correct it.

100 visibility checks per month. No credit card.

Book Briefing

For procurement and Enterprise scope.

The Public AI problem for insurance

What public AI says about insurers today.

Public AI answer engines — ChatGPT, Gemini, Google AI Overview, Copilot, Perplexity — answer questions about your policies every day. Some of those answers are incomplete, outdated, or wrong: misstated coverage scope, premium disclosure errors, free-look period confusion, claims process inaccuracy. Wrong answers travel as if they were yours.

Common factual gaps for insurance-related answers include policy coverage scope misstatements (what is and isn't covered), premium disclosure accuracy, free-look / cooling-off period accuracy (especially MY 15-day / SG 14-day), claims process step and documentation accuracy, Takaful vs conventional product confusion, and surrender value calculation accuracy. Different engines fail differently.

For a regulated insurer, what an answer engine says about your policies is functionally what a customer hears about you. Misstatements travel through customer-service inquiries, complaints, social media, and — increasingly — into consumer protection visibility. The accuracy gap is a reputation, distribution, and compliance issue at the same time. Most insurers today have no systematic way to see what these engines are saying.

What Lawnise does for insurance

Public AI governance, mapped to insurance risk.

Lawnise is AI visibility, accuracy, reputation, and risk infrastructure — built for regulated enterprises that need to see and correct what public AI says about them. Four capability dimensions, mapped here to the questions that matter most for insurers.

01Visibility

Visibility — what each engine is saying about your policies

Lawnise runs visibility checks against the supported public AI engines on a continuous schedule. You see, prompt by prompt, which engines are talking about your policies, what share of voice you have versus competitors, and where your brand is being missed. For insurance specifically: policy-comparison prompts, premium-quote prompts, claims-process prompts, and agent / branch / direct-channel prompts are tracked as a default sector pack.

02Accuracy

Accuracy — fact verification against your source-of-truth

Every claim a public AI engine makes about your policies is checked against your stored brand reference documents — policy schedules, current premium tables, regulatory filings, public statements. Where the engine answer disagrees with your reference, the discrepancy is flagged with the exact engine response, the supporting source, and a hash-linked evidence trail. Fact verifications are part of the Lawnise Monitor and Enterprise plans.

03Reputation

Reputation — how the engines are characterising your insurer

Reputation analysis tracks how public AI answers describe your insurer's posture on the issues that customers ask about: claims handling, premium fairness, digital experience, complaint resolution, sustainability framing. Sentiment shifts are tracked over time and against your competitor set. Reputation analysis is included in Monitor's combined visibility + reputation scan quota.

04Risk

Risk — compliance coverage and correction pathways

Where engine answers create compliance exposure (incorrect product disclosures, misstatements about regulated terms), Lawnise Enterprise customers can generate compliance coverage tailored to insurance-sector regulators, export the full evidence-to-claim ledger for audit, and use the right-to-reply workflow plus correction-notice publishing to push corrected answers back into the public record.

Capability detail by dimension lives at /platform; sector-relevant evidence lives in the next section.

Lawnise Trust Index

Lawnise Trust Index — insurance coverage.

The Lawnise Trust Index is an independent benchmark of how accurately public AI engines describe regulated institutions. Trust Index coverage and methodology are in preparation for the P4 Authority Launch. Insurers interested in inclusion in the launch sample frame can Book Briefing.

Who this is for

Who this is for.

Lawnise is built for the cross-functional team that owns external-AI exposure inside an insurer.

ACISO / Head of Information Security

What your insurer's customers hear from public AI engines is part of your external attack surface. Lawnise gives you continuous visibility plus the evidence trail your security team needs to scope and respond.

BCRO / Head of Risk

Inaccurate AI answers about regulated products are a live operational and compliance risk. Lawnise quantifies that exposure across engines, correlates it to regulatory framework, and tracks correction over time.

CHead of Compliance

When public AI engines misstate your policy disclosures, you need a defensible record of what was wrong, when, on which engine, and what corrective action was taken. The Lawnise evidence-to-claim ledger is built for that record.

DHead of Distribution / Agency Channel

When engine answers misstate your policy terms, premium calculations, or claims process, the distortion lands inside the broker and agent channel where quotes get won and lost. Lawnise tracks engine-channel exposure so distribution leadership can see and correct what's reaching prospects through AI surfaces.

Product proof

What it looks like.

Three illustrative scenarios showing how Lawnise surfaces and corrects engine answers about an insurer. Fixture institution; numbers and prompts are illustrative, not customer data.

IllustrativeFixture institution “InsuranceCo MY”. Prompts, engine responses, and rates below are constructed for illustration only — not derived from any real institution’s data or any specific engine output.

Illustrative · 01

Scenario 01 · Visibility check pattern

Same prompt, three engines, three different answers.

“What is the free-look period for InsuranceCo MY's endowment plan?”

Engine	Response pattern	Verdict
AEngine A	States a free-look window that does not match the institution’s published policy schedule.	Factual gap
BEngine B	States the matching free-look period (correct against stored policy schedule).	Match
CEngine C	Does not surface the institution; returns a competitor’s policy free-look instead.	Visibility gap

Illustrative prompt: "What is the free-look period for InsuranceCo MY's endowment plan?"

Engine A states a period that does not match the institution's published policy schedule (factual gap). Engine B states the matching period (correct against stored reference). Engine C does not surface the institution; returns a competitor's free-look period instead (visibility gap).

A Lawnise visibility check captures the same prompt across all engines on the same scan; fact verification flags the discrepant engine answer against the stored policy schedule; reputation analysis logs the competitor-displacement signal.

Illustrative · 02

Scenario 02 · Evidence-to-claim ledger pattern

Hash-linked chain from engine response to reference document.

“InsuranceCo MY offers a free-look period of [N] days on endowment policies.”

Engine responseCaptured verbatim from the engine answer at scan time, with full context snapshot.

Stored referenceCurrent published policy schedule + free-look regulatory minimum (MY: 15 days; SG: 14 days).

Hash-linked trailscan_id→engine_response_hash→reference_doc_version→multi_agent_review_path→timestamp

Audit exportEach row carries the full chain — engine response, reference matched, scan metadata, verification path.

Illustrative engine response: "InsuranceCo MY offers a free-look period of [N] days on endowment policies."

Stored reference: current published policy schedule + free-look regulatory minimum (MY: 15 days; SG: 14 days).

Hash-linked evidence trail: scan ID + engine response capture + reference document version + multi-agent review path + timestamp.

Enterprise customers export this ledger for audit. Each row carries the full chain — engine response, reference matched, scan metadata, verification path.

Illustrative · 03

Scenario 03 · Correction workflow pattern

Finding to correction notice to engine update to re-scan.

“A discrepancy on Engine X for InsuranceCo MY about claims process documentation is flagged.”

Step 01Finding flagged

Discrepancy on Engine X surfaces in scan; Lawnise opens a finding with full evidence chain.

Claims documentation

Step 02Correction notice drafted

Right-to-reply workflow drafts a correction notice anchored to the institution’s own reference.

Right-to-reply

Step 03Notice sent to engine

Notice published via the engine provider’s correction pathway; receipt logged in the ledger.

Correction pathway

Step 04Re-scan verification

Lawnise re-scans the same prompt set; verification status updates from gap to match (or escalates).

Closed-loop verify

Illustrative cycle: a discrepancy on Engine X for InsuranceCo MY about claims process documentation is flagged. The right-to-reply workflow drafts a correction notice; the notice is sent to the engine provider via the published correction pathway; Lawnise tracks the cycle from finding → correction notice → engine update → re-scan verification.

Start with what fits

Start with what fits.

See what public AI says about your policies in 60 seconds. Or scope a briefing for procurement and Enterprise.

100 visibility checks per month. No credit card.

Book Briefing

For procurement, Enterprise scope, and Lawnise-operated deployment.

Lawnise builds independent AI trust infrastructure for regulated sectors, starting with banking and insurance. When independence and methodology transparency matter, the answer engine answers should match the source of truth.
Insurance-sector teams use Lawnise to operationalise the Enterprise Public AI TRiSM for insurance framework, with platform evidence grounded in our ongoing public AI audit methodology.
See the underlying Public-facing AI governance and evidence infrastructure that powers visibility, accuracy, and reputation checks for regulated brands.
Adjacent-sector procurement teams may also review Enterprise Public AI TRiSM for banking for cross-sector coverage patterns.

StickyBook Briefing